Even if \({\mathcal {H}}\)  is known, minimizing \(\ell (\Theta )\)  with respect to \(\Theta \)  entails solving a non-convex optimization problem.
Under the assumption that the function \(\Psi (\cdot \, ; \theta )\)  in (REF ) is differentiable with respect to \(\theta \)  and leveraging the fact that we have access to samples of \({\mathcal {H}}\)  (cf. Section ), we seek to minimize (REF ) through stochastic gradient descent.
It should be noted that, unlike [1]} and in accordance with some recent works [2]}, [3]}, UWMMSE is an unsupervised method since it requires access to samples of the channel state matrices \({\mathbf {H}}\)  but does not require access to the optimal power allocations (labels) associated with those channels.
